





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
	import torch

	from transformers import AutoModelForImageClassification

if is_vision_available():
	from transformers import AutoImageProcessor




@require_torch
@require_vision
class 	UpperCamelCase_							(  unittest.TestCase     ):


							@slow
							def   _snake_case	(				self     :Dict		) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""		)
									SCREAMING_SNAKE_CASE__								=   AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""		)
									model.to(__A		)

									from datasets import load_dataset

									SCREAMING_SNAKE_CASE__								=   load_dataset("""nielsr/rvlcdip-demo"""		)

									SCREAMING_SNAKE_CASE__								=   dataset["""train"""][0]["""image"""].convert("""RGB"""		)

									SCREAMING_SNAKE_CASE__								=   image_processor(__A				,					return_tensors="""pt"""		).to(__A		)

									# forward pass
									with torch.no_grad():
											SCREAMING_SNAKE_CASE__								=   model(**__A		)
											SCREAMING_SNAKE_CASE__								=   outputs.logits

									SCREAMING_SNAKE_CASE__								=   torch.Size((1, 16)		)
									self.assertEqual(logits.shape				,					__A		)

									SCREAMING_SNAKE_CASE__								=   torch.tensor(
									    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]				,					device=__A				,					dtype=torch.float				,					)
									self.assertTrue(torch.allclose(logits[0, :3]				,					__A				,					atol=1E-4		)		)

lowerCamelCase_    :			Tuple    =	{
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
lowerCamelCase_    :			Union[str, Any]    =	{
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 12,
    """Pm""": 15,
    """Em""": 18,
    """Zm""": 21,
    """Ym""": 24,
}







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						float:
				UpperCamelCase_:				Any						=    from_type.lower().strip("""s"""    )
				UpperCamelCase_:				int						=    to_type.lower().strip("""s"""    )

				UpperCamelCase_:				Any						=    UNIT_SYMBOL.get(lowerCamelCase  ,     lowerCamelCase    )
				UpperCamelCase_:				str						=    UNIT_SYMBOL.get(lowerCamelCase  ,     lowerCamelCase    )

				if from_sanitized not in METRIC_CONVERSION:
								UpperCamelCase_:				Optional[int]						=    (
								    F'''Invalid \'from_type\' value: {from_type!r}.\n'''
								    F'''Conversion abbreviations are: {", ".join(lowerCamelCase    )}'''
								)
								raise ValueError(lowerCamelCase    )
				if to_sanitized not in METRIC_CONVERSION:
								UpperCamelCase_:				Dict						=    (
								    F'''Invalid \'to_type\' value: {to_type!r}.\n'''
								    F'''Conversion abbreviations are: {", ".join(lowerCamelCase    )}'''
								)
								raise ValueError(lowerCamelCase    )
				UpperCamelCase_:				Union[str, Any]						=    METRIC_CONVERSION[from_sanitized]
				UpperCamelCase_:				str						=    METRIC_CONVERSION[to_sanitized]
				UpperCamelCase_:				Tuple						=    1

				if from_exponent > to_exponent:
								UpperCamelCase_:				Union[str, Any]						=    from_exponent - to_exponent
				else:
								UpperCamelCase_:				Dict						=    -(to_exponent - from_exponent)

				return value * pow(10  ,     lowerCamelCase    )


if __name__ == "__main__":
							from doctest import testmod

							testmod()






